Predicting Chaos Most of the Time from Embeddings with Self-Intersections

Embedding techniques for predicting chaotic time series from experimental data may fail if the reconstructed attractor self-intersects, and such intersections often occur unless the embedding dimension exceeds twice the attractor's box counting dimension. Here we consider embeddings with self-intersection.